ruRoberta-distilled
Model was distilled from ai-forever/ruRoberta-large with ❤️ by me.
Usage
from transformers import pipeline
pipe = pipeline('feature-extraction', model='d0rj/ruRoberta-distilled')
tokens_embeddings = pipe('Привет, мир!')
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('d0rj/ruRoberta-distilled')
model = AutoModel.from_pretrained('d0rj/ruRoberta-distilled')
def embed_bert_cls(text: str) -> torch.Tensor:
t = tokenizer(text, padding=True, truncation=True, return_tensors='pt').to(model.device)
with torch.no_grad():
model_output = model(**t)
embeddings = model_output.last_hidden_state[:, 0, :]
embeddings = torch.nn.functional.normalize(embeddings)
return embeddings[0].cpu()
embedding = embed_bert_cls('Привет, мир!')
Logs
Distillation process lasts for 120 hours on 4 Nvidia V100.
See all logs at WandB.
Configuration changes
- Activation GELU -> GELUFast
- Attention heads 16 -> 8
- Hidden layers 24 -> 6
- Weights size 1.42 GB -> 464 MB
Data
Overall: 9.4 GB of raw texts, 5.1 GB of binarized texts.
Only texts in Russian were used for distillation. I do not know how the model behaves in Englishю
Used data:
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